Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
By the amendment claims 1-20 are pending with claims 1-2, 5, 9-13, 16 and 20 being amended. The applicant’s amendments have overcome the 35 U.S.C 101 rejection as outlined in the previous Office action.
Information Disclosure Statement
The Information Disclosure Statement filed on April 13th, 2026 has been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: battery sorting mechanism. Battery sorting mechanism is interpreted as any one of the list of options comprising pneumatic actuators, guides, hoists, cranes, platforms, or alternative means for diverting batteries into respective bins of the sorting bins 108 (P0044).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-6, 8-14 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young et al. (US 11747290), hereafter Young, in view of Wagner et al. (US 20200151407) hereafter Wagner.
With regards to claim 1, Young discloses a computer-implemented method (Abstract, method 110 Fig. 11) comprising: receiving, from a plurality of sensors (chemical sensing device 110, physical sensing device 150), a plurality of signals corresponding to a target battery (105); determining, utilizing a classifier machine learning model (Col. 10 L20-25), a predicted battery classification of the target battery from a plurality of battery classifications based on the plurality of signals (Col. 9, L60-62); and to actuating a battery sorting mechanism (sorting device 120) to transfer the target battery into a bin corresponding to the predicted battery classification of the target battery. Young also discloses another embodiment that senses weight, shape/size, and chemistry (see Figure 5) but does not directly disclose that the sensor is a 3D scanner.
However, Wagner discloses using a 3D scanner to image items (P0121). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the invention to replace the physical sensing device of Young with the 3D scanner as disclosed by Wagner in order to more accurately identify the battery.
With regards to claim 2, Young and Wagner disclose all the elements of claim 1 as outlined above. Young further discloses wherein the plurality of sensors further comprises two or more of an x-ray scanning array (Col. 6, L38-39), an RGB camera (Col. 7, L27-29), or an infrared camera.
With regards to claim 3, Young and Wagner disclose all the elements of claim 1 as outlined above. Young further discloses determining material attributes of the target battery based on one or more x-ray attenuation measurements received from an x-ray scanning array for the target battery (Col. 6, L22-34); wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises determining the predicted battery classification of the target battery based at least in part on the material attributes (Col. 5, L62-Col. 6 L2).
With regards to claim 4, Young and Wagner disclose all the elements of claim 3 as outlined above. Young does not directly disclose capturing a first set of x-ray attenuation measurements utilizing a first x-ray scanning array oriented at a first orientation relative to the target battery; and capturing a second set of x-ray attenuation measurements utilizing a second x-ray scanning array oriented at a second orientation relative to the target battery; wherein determining the material attributes of the target battery based on the one or more x-ray attenuation measurements comprises determining the material attributes based on the first set of x-ray attenuation measurements and the second set of x-ray attenuation measurements.
However, this is a simple duplication of parts and therefore rendered obvious to a person with ordinary skill in the art before the effective filing date of the invention, in order to ensure a more accurate sort (MPEP 2144.04.VI.B).
With regards to claim 5, Young and Wagner disclose all the elements of claim 1 as outlined above. Wagner further discloses wherein the 3D scanner comprises one or more of a 3D laser scanner, a structured-light scanner, or a time-of-flight scanner (P0120).
With regards to claim 6, Young and Wagner disclose all the elements of claim 1 as outlined above. Young further discloses determining a plurality of printed characters or codes disposed on the target battery based on image data of the target battery received from one or more RGB cameras (Col. 7, L29-37); wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises determining the predicted battery classification of the target battery based at least in part on the plurality of printed characters or codes (Col. 7, L31-37).
With regards to claim 8, Young and Wagner disclose all the elements of claim 1 as outlined above. Young further discloses wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery from the plurality of battery classifications based on the plurality of signals comprises utilizing an object detection neural network to determine the predicted battery classification from one or more images of the target battery (Col. 10, L4-L13).
With regards to claim 9, Young discloses a system (Abstract) comprising: one or more memory devices comprising a classifier machine learning model (Col. 10, L25-28) and a plurality of battery classifications (Col. 9, L47-57); and one or more processors (compute device 120) configured to cause the system to perform operations comprising: determining one or more attributes of a target battery from a plurality of signals from a plurality of sensors (Col. 9, L60-62; Col. 10 L20-25) and determining, utilizing the classifier machine learning model, a predicted battery classification of the target battery from a plurality of battery classifications based on the determined one or more attributes of the target battery (Col. 9, L60-62); and actuating a battery sorting mechanism (sorting mechanism 120) to transfer the target battery into a bin corresponding to the predicted battery classification of the target battery. Young also discloses another embodiment that senses weight, shape/size, and chemistry (see Figure 5) but does not directly disclose that the sensor is a 3D scanner.
However, Wagner discloses using a 3D scanner to image items (P0121). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the invention to replace the physical sensing device of Young with the 3D scanner as disclosed by Wagner in order to more accurately identify the battery.
With regards to claim 10, Young and Wagner disclose all the elements of claim 9 as outlined above. Young further discloses indicating, to a battery sorting mechanism, the predicted battery classification of the target battery to cause actuation of the battery sorting mechanism to transfer the target battery into the bin corresponding to the predicted battery classification (Col. 10, L25-28).
With regards to claim 11, Young and Wagner disclose all the elements of claim 9 as outlined above. Young further discloses wherein: the operations further comprise receiving one or more label images and one or more profile images of the target battery from a RGB camera of the plurality of sensors (Col. 7, L29-38); determining the one or more attributes of the target battery comprises: determining, utilizing optical character recognition, the printed characters of the target battery from the one or more label images (Col. 7, L29-38); and determining the form factor of the target battery from the one or more profile images (Col. 7, L31-37); and determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery is based on the printed characters and the form factor (Col. 7, L8-17).
With regards to claim 12, Young and Wagner discloses all the elements of claim 9 as outlined above. Young further discloses wherein: determining the one or more attributes of the target battery comprises: the dimensions of the target battery based on the 3D scan data from the 3D scanner of the plurality of sensors; and determining a battery chemistry of the target battery based on x-ray attenuation data from an x-ray scanning array of the plurality of sensors (Col. 6, L22-34); and determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery is based on the battery chemistry ((Col. 5, L62-Col. 6 L2). Young does not disclose the determining dimensions of the target battery based on the 3D scan data from the 3D scanner of the plurality of sensors and determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery is based on the battery chemistry and dimensions. However, Wagner discloses determining dimensions using a 3D scanner (P0120). Therefore, the combination of Young and Wagner discloses this feature.
With regards to claim 13, Young and Wagner discloses all the elements of claim 9 as outlined above. Young further discloses receiving, from the plurality of sensors, additional signals corresponding to additional batteries; and determining, utilizing the classifier machine learning model, additional predicted battery classifications of the additional batteries from the plurality of battery classifications based on the additional signals (Col.5 L53-57).
With regards to claim 14, Young and Wagner disclose all the elements of claim 13 as outlined above. Young further discloses indicating, to a battery sorting mechanism, the predicted battery classification of the target battery and the additional predicted battery classifications of the additional batteries; and individually transferring each battery of the target battery and the additional batteries, utilizing the battery sorting mechanism, into a plurality of bins respectively associated with the plurality of battery classifications (Col. 6, L3-12).
With regards to claim 16, Young discloses a non-transitory computer-readable medium storing executable instructions (Col. 9, L50-54), which when executed by at least one processor (commute device 120), cause the at least one processor to perform operations comprising: receiving, from a plurality of sensors, a plurality of signals corresponding to a target battery (chemical sensing device 110, physical sensing device 150); determining, utilizing a classifier machine learning model (Col. 10 L20-25), a predicted battery classification of the target battery from a plurality of battery classifications based on the plurality of signals (Col. 10, L25-28); and actuating a battery sorting mechanism to transfer the target battery into a bin corresponding to the predicted battery classification of the target battery (Col. 9, L60-62, sorting mechanism 120). Young also discloses another embodiment that senses weight, shape/size, and chemistry (see Figure 5) but does not disclose receiving, from a plurality of sensors including a three-dimensional (3D) scanner, a plurality of signals corresponding to a target battery; determining dimensions of the target battery based on 3D scan data received from the 3D scanner.
However, Wagner discloses using a 3D scanner to image items (P0121). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the invention to replace the physical sensing device of Young with the 3D scanner as disclosed by Wagner in order to more accurately identify the battery.
With regards to claim 17, Young and Wagner disclose all the elements of claim 16 as outlined above. Young further discloses capturing one or more label images utilizing an RGB camera of the plurality of sensors; and identifying, utilizing object character recognition (OCR), a plurality of printed characters or codes from the one or more label images (Col. 7, L29-37).
With regards to claim 18, Young and Wagner disclose all the elements of claim 17 as outlined above. Young further discloses capturing one or more profile images of the target battery utilizing one or more RGB cameras of the plurality of sensors; and determining, based on the one or more profile images of the target battery, a form factor of the target battery(Col. 7, L8-17).
With regards to claim 19, Young and Wagner disclose all the elements of claim 18 as outlined above. Young further discloses capturing x-ray attenuation data for the target battery utilizing an x-ray scanning array of the plurality of sensors (Col. 6, L22-34).
With regards to claim 20, Young and Wagner disclose all the elements of claim 19 as outlined above. Young further discloses wherein determining, utilizing the classifier machine learning model, the predicted battery classification of the target battery based on the plurality of signals comprises utilizing a decision tree to determine the predicted battery classification based on the x-ray attenuation data, the form factor of the target battery, and the plurality of printed characters or codes from the one or more label images (Col. 6, L61-Col. 7, L3).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Young and Wagner as applied to claim 1 above, and further in view of Holopainen et al. (US 20230405639), hereafter Holopainen.
With regards to claim 7, Young and Wagner disclose all the elements of claim 1 as outlined above. Young and Wagner do not disclose determining a temperature or a temperature gradient of the target battery from signals received from an infrared camera for the target battery; and in response to determining that the temperature or the temperature gradient is above a threshold value, activating an alarm or transferring the target battery into a safety bin.
Holopainen discloses sorting objects based on temperature (P0041; P0049). It would have been obvious to a person with ordinary skill in the art before the effective filing date of the invention to sort based on temperature sensor as disclosed by Holopainen in order to sort out defective items.
Allowable Subject Matter
Claim 15 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 15 would be allowable for disclosing “and in response, transferring the at least one battery into a dunk tank containing a fire retardant.” Although US 20220416323 discloses sounding an alarm for a hazardous anomaly, there is no teaching or suggestion that would render it obvious to a person with ordinary kill in the art before the effective filing date of the invention to use a dunk tank as recited.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LYNN BURKMAN whose telephone number is (571)272-5824. The examiner can normally be reached M-Th 7:30am to 6:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael McCullough can be reached at (571)272-7805. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/J.L.B./Examiner, Art Unit 3653
/MICHAEL MCCULLOUGH/Supervisory Patent Examiner, Art Unit 3653
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